Over the years, various hematologic, chemical, and other laboratory analytical instruments have become increasingly more sophisticated. With the realization of computers and automation, these instruments have the ability to generate tremendous amounts of numerical data which need to be analyzed and interpreted by human operators.
Humans, however, are only capable of retaining and recalling, on the average, about five numbers at a given time. In situations where decisions or diagnoses must be made based on the interrelationship among multiple numbers, the human utilization range falls to only two to three numbers. Such a small utilization range severely restricts the human operator's ability to use the full potential and capabilities of the increasingly sophisticated analytical instruments.
In contrast, humans have little difficulty making decisions based on visual clues, such as graphical shapes (e.g., we can easily distinguish a car from a horse; a yield sign from a stop sign). One conventional approach that uses human abilities to recognize patterns is the projection of multi-dimensional patterns in a two-dimensional or pseudo three-dimensional plane. This method requires a tremendous amount of computation and effort to ascertain which plane provides the most information. Another well-known graphical representation of multiple numbers is the use of "Chernoff Faces", as described by Dr. Herman Chernoff in "The Use of Faces to Represent Points in k-Dimensional Space Graphically", Journal of the American Statistical Association, June, 1973, Volume 68, pp. 361-368. In this method, data is represented by a cartoon of a face whose features, such as length of the nose and curvature of the mouth, correspond to multivariate observations.
This system, however, lacks the ability to generate a point of reference or clear clinical decision levels. Therefore, once the test results are translated into facial features, no meaningful comparison can be made by the user between the values of the test results and any reference value or clinical decision level. In addition, some people find that using funny faces to represent data can be distracting and not in good taste, especially when the faces represent data corresponding to different ill patients.
Other popular methods involve using polygon or star based representations of parameters. In general, these methods begin with a central point and a plurality of radial axes at angles of 2.pi./n, where n represents the number of parameters to be displayed. Each parameter value is then represented by a representation point on each axis, with the distance from the center point to the representation point reflecting the magnitude of the parameter. A polygon or a star is then constructed by connecting the representation points of all the displayed parameter values.
Unlike the Chernoff method, star-based representations offer a quick comparison of a test result with its "normal range." In other words, a standard or "universal" diagram, indicating a normal range of values for a particular disease state, can be generated using empirical values and published data for a given disease condition. This allows a user to compare test data to the standard in order to determine a particular condition quickly. However, star-based methods also contain sharp spikes due to abnormally large values that appear on the resulting diagrams which divert human attention from the overall shape of the figure. Moreover, an individual parameter with an usually large value is capable of dominating, and sometimes preventing, all other parameters from forming the shape of the diagram.
Thus, there remains a need for a method and system to produce a diagram representing a plurality of data that is easy to comprehend, that is capable of restricting values that would otherwise dominate a set of data, and that makes it easy for the viewer to focus on an entire diagram.